Tag Archives: Genetic Diversity

Time flies… in the blink of an eye! And even more so in science. The molecular lab work we were used to two decades ago seems like ancient history to today’s PhD students. The speed of change in sequencing technology is so overwhelming that imagination usually fails to foresee how our daily work will be in 10 years’ time. But in the field of biodiversity assessment, we have very good clues. Next Generation Sequencing is quickly becoming our workhorse for ambitious projects of species and genetic inventories.

One by One Approach to Studying Biodiversity

For decades, most initiatives measured biodiversity in the same way: collect a sample of many individuals in the field, sort the specimens, identify them to a Linnaean species one at a time (if there was a good taxonomist in the group which, unfortunately, it is kind of lucky these days!), and count them. Or, if identification was based on molecular data, the specimen was subject to DNA extraction, to sequence one (or several) short DNA markers. This involved countless hours of work that could be saved if, instead of inventorying biodiversity specimen-by-specimen, we followed a sample-by-sample approach. To do this now, we just have to make a “biodiversity soup”.

To understand how species survive in nature, demographers pair field-collected life history data on survival, growth and reproduction with statistical inference. Demographic approaches have significantly contributed to our understanding of population biology, invasive species dynamics, community ecology, evolutionary biology and much more.

As ecologists begin to ask questions about demography at broader spatial and temporal scales and collect data at higher resolutions, demographic analyses and new statistical methods are likely to shed even more light on important ecological mechanisms.

Population Processes

Traditionally, demographers collect life history data on species in the field under one or more environmental conditions. This approach has significantly improved our understanding of basic biological processes. For example, rosette size is a significant predictor of survival for plants like wild teasel (Werner 1975 – links to all articles are at the end of the post), and desert annual plants hedge their bets against poor years by optimizing germination strategies (Gremer & Venable 2014).

Demographers also include temporal and spatial variability in their models to help make realistic predictions of population dynamics. We now know that temporal variability in carrying capacity dramatically improves population growth rates for perennial grasses and provides a better fit to data than models with varying growth rates because of this (Fowler & Pease 2010). Moreover, spatial heterogeneity and environmental stochasticity have similar consequences for plant populations (Crone 2016). Continue reading →

Biodiversity Indicators are some of the most important tools linking ecological data with government policy. Indicators need to summarise large amounts of information in a format that is accessible to politicians and the general public. The primary use of indicators is to monitor progress towards environmental targets. For the UK, a suite of indicators are produced annually which are used to monitor progress towards the Aichi targets of the Convention on Biological Diversity as well as for European Union based commitments. However, this is complicated by the fact that biodiversity policy within the UK is devolved to each of the four nations, so additional indicators have been developed to monitor the commitments of each country.

A range of biodiversity indicators exist within this suite covering the five strategic goals of the Convention; which include addressing the causes of biodiversity loss, reducing pressures on biodiversity and improving status of biodiversity within the UK. Within strategic goal C (improve status of biodiversity by safeguarding ecosystems, species and genetic diversity) there are currently 11 “State” indicators that use species data to monitor progress towards the targets underlying this goal. Most existing species based indicators use abundance data from large scale monitoring schemes with systematic protocols. However, there are other sources of data, such as occurrence records, that can offer an alternative if they are analysed using the appropriate methods. This post will discuss the development of species indicators for occurrence records to complement the current UK species based indicators, specifically relating to the C4b priority species indicator and the D1c pollinators indicator. Continue reading →